To solve the given optimization problem using Gurobi, we first need to understand and possibly simplify or clarify the objective function and constraints provided.

The objective function aims to maximize:
\[6(x_0)^2 + 5x_0x_1 + 9(x_1)^2 + 3x_1x_2 + 6(x_2)^2 + x_0\]

where \(x_0\) represents the quantity of reconnaissance troops, \(x_1\) represents the quantity of logistics companies, and \(x_2\) represents the quantity of medical platoons.

The constraints can be summarized as follows:
1. Logistical capacity constraints for each type of unit.
2. Deployment weight constraints for each type of unit.
3. Combined logistical capacity constraints for different combinations of units.
4. Combined deployment weight constraints for different combinations of units.
5. Integer constraints for the quantities of all types of units.

Given these, let's formulate the problem in Gurobi Python code:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="reconnaissance_troops")
x1 = m.addVar(vtype=GRB.INTEGER, name="logistics_companies")
x2 = m.addVar(vtype=GRB.INTEGER, name="medical_platoons")

# Objective function
m.setObjective(6*x0**2 + 5*x0*x1 + 9*x1**2 + 3*x1*x2 + 6*x2**2 + x0, GRB.MAXIMIZE)

# Constraints
# Logistical capacity constraints
m.addConstr(2.32*x0 + 4.68*x1 + 1.72*x2 <= 17, "log_cap_total")
m.addConstr(2.32*x0 + 1.72*x2 <= 26, "log_cap_recon_med")
m.addConstr(2.32*x0 + 4.68*x1 <= 17, "log_cap_recon_log")

# Deployment weight constraints
m.addConstr(4.51*x0 + 4.38*x1 + 2.0*x2 <= 29, "deploy_weight_total")
m.addConstr(4.51*x0 + 2.0*x2 <= 29, "deploy_weight_recon_med")
m.addConstr(4.38*x1**2 + 2.0*x2**2 <= 35, "deploy_weight_log_med")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Reconnaissance Troops: {x0.x}")
    print(f"Logistics Companies: {x1.x}")
    print(f"Medical Platoons: {x2.x}")
else:
    print("No optimal solution found.")
```